The association between drug injection duration and hepatitis C prevalence among people who inject drugs in Iran

People who inject drugs (PWID) are at higher risk of hepatitis C virus (HCV) due to their behaviors such as shared injection. Employing appropriate modeling approaches is crucial for accurately evaluating the impact of other variables on outcomes, in this case, HCV seropositivity. This study aimed to assess the non-linear effect of injection duration on HCV seropositivity. From July 2019 to March 2020, 2,684 PWID in Iran were recruited. The binary outcome variable was HCV serostatus (positive vs. negative), determined by detecting HCV antibodies. The non-linear effect of injection duration on HCV seropositivity was assessed using a multilevel Generalized Additive Model in R software, adjusting the effects of other variables in the analysis. We found a non-linear effect of injection duration on HCV seropositivity status (p-value < 0.001). The probability of HCV seropositivity increased with injection duration, though this relationship was non-linear. Initially, the probability rises faster; however, this effect diminishes as the injection duration extends. An initial sharp increase in HCV risk was seen during the first 20 years of injection. HCV seropositivity was notably associated with ever HIV seropositivity (OR [Odds Ratio] = 10.54, 95% CI [Confidence Interval]: 5.39, 20.61, p-value < 0.001), ever having injected methamphetamine (OR = 1.72, 95% CI: 1.33, 2.22, p-value < 0.001), being currently married (OR = 0.67, 95% CI: 0.48, 0.93, p-value = 0.018), ever shared needle/syringe with others (OR = 2.63, 95% CI: 1.32, 5.22, p-value = 0.006), and ever being incarcerated (OR = 1.97, 95% CI: 1.50, 2.58, p-value < 0.001). Our study contributes to the field by demonstrating that a non-linear approach can reveal patterns of risk that linear models might fail to capture. These findings indicate that the relationship between injection duration and HCV seropositivity can be more complex than previously understood, underscoring the importance of employing more advanced modeling techniques in future research.